Here we present a recurrent neural network model based on evidence that neuronal activity in the SEF encodes saccade location and initiation timing during learned saccade sequences (Isoda & Tanji, 2004). In our model, the learned saccade sequence is retrieved from “memory traces” that encode the locations of the targets and triggering times of each saccade. During retrieval, the neural activity at a given spatial location increases, and is modulated by the memorized target locations through successive iterations until it crosses an exponentially decreasing threshold and triggers the saccade. Time-related parameters in the memory determine the rise of neural activity corresponding to each target location. By changing the weights applied to each target location and initiation timing, the model was able to replicate the findings that microstimulation applied the supplementary eye field can alter saccade selection to temporally offset targets (Histed & Miller, 2005) and can modulate saccade latency (Yang & Heinen, 2004). The model also predicts that higher variance in saccade endpoint should occur for later saccades in a sequence, and that there should be a greater likelihood of sequencing errors when two saccade targets occur in spatial and temporal proximity. The simulation results suggest that multiple saccade targets can be encoded in a single SEF network to be retrieved and executed in order and at the proper time.